Skills de Claude Code · página 129

Creates new Architecture Decision Record (ADR) documents for significant architectural changes using a consistent template and repository-aware naming and storage guidance. Use when a user or agent decides on an architectural change, needs to document technical rationale, or wants to add a new ADR to the project history.

Generates a structured Bug Fix Brief (BFB) to document issue corrections. Includes root cause analysis, repro steps, fix options, and fix checklist. Use when user asks to create a BFB, document a bug fix, or generate a bug correction document.

Provides automated documentation updates by analyzing git changes between the current branch and the last release tag. Performs git diff analysis to identify modifications, then updates README.md, CHANGELOG.md following Keep a Changelog standard, and discovers documentation folders for contextual updates. Use when preparing a release, maintaining documentation sync, or before creating a pull request. Triggers on "update docs", "update changelog", "sync documentation", "update readme", "prepare release documentation".

Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider specifics, (3) BPMN process diagrams, (4) UML diagrams (class, sequence, activity), (5) data flow diagrams (DFD), (6) decision flowcharts, or (7) system interaction diagrams. This skill focuses on generic/abstract representations, not AWS/Azure-specific architectures (use aws-drawio-architecture-diagrams for cloud diagrams).

Provides a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for implementation.

Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

Provides AWS Lambda integration patterns for Java with cold start optimization. Use when deploying Java functions to AWS Lambda, choosing between Micronaut and Raw Java approaches, optimizing cold starts below 1 second, configuring API Gateway or ALB integration, or implementing serverless Java applications. Triggers include "create lambda java", "deploy java lambda", "micronaut lambda aws", "java lambda cold start", "aws lambda java performance", "java serverless framework".

Provides patterns to configure AWS RDS (Aurora, MySQL, PostgreSQL) with Spring Boot applications. Configures HikariCP connection pools, implements read/write splitting, sets up IAM database authentication, enables SSL connections, and integrates with AWS Secrets Manager. Use when setting up RDS connections in Spring Boot, configuring connection pooling, or managing database credentials securely.

Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.

Provides AWS SDK for Java 2.x client configuration, credential resolution, HTTP client tuning, timeout, retry, and testing patterns. Use when creating or hardening AWS service clients, wiring Spring Boot beans, debugging auth or region issues, or choosing sync vs async SDK usage.

Provides Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.

Provides AWS Key Management Service (KMS) patterns using AWS SDK for Java 2.x. Use when creating/managing encryption keys, encrypting/decrypting data, generating data keys, digital signing, key rotation, or integrating encryption into Spring Boot applications.

Provides AWS Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.

Provides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.

Provides AWS RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.

Provides Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific configurations.

Provides AWS Secrets Manager patterns for AWS SDK for Java 2.x, including secret retrieval, caching, rotation-aware access, and Spring Boot integration. Use when storing or reading secrets in Java services, replacing hardcoded credentials, or wiring secret-backed configuration into applications.

Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Java 21+ Spring Boot 3.5+ applications. Use when structuring layered architectures, separating domain logic from frameworks, implementing ports and adapters, creating entities/value objects/aggregates, or refactoring monolithic codebases for testability and maintainability.

Provides expert guidance for building GraalVM Native Image executables from Java applications. Use when converting JVM applications to native binaries, optimizing cold start times, reducing memory footprint, configuring native build tools for Maven or Gradle, resolving reflection and resource issues in native builds, or implementing framework-specific native support for Spring Boot, Quarkus, and Micronaut. Triggers include "graalvm native image", "native executable java", "java cold start optimization", "native build tools", "ahead of time compilation java", "reflection config graalvm", "native image build failure".

Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.

Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.

Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.

Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.

Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.

Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.

Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services. Use when setting up monitoring, health checks, or metrics for Spring Boot applications.

Provides patterns for implementing Spring Boot caching: configures Redis/Caffeine/EhCache providers with TTL and eviction policies, applies @Cacheable/@CacheEvict/@CachePut annotations, validates cache hit/miss behavior, and exposes metrics via Actuator. Use when adding caching to Spring Boot services, configuring cache expiration, evicting stale data, or diagnosing cache misses.

Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.

Provides dependency injection patterns for Spring Boot projects, including constructor-first design, optional collaborator handling, bean selection, and wiring validation. Use when creating services and configurations, replacing field injection, or troubleshooting ambiguous or fragile Spring wiring.

Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.

Provides patterns to generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.

Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.

Provides fault tolerance patterns for Spring Boot 3.x using Resilience4j. Use when implementing circuit breakers, handling service failures, adding retry logic with exponential backoff, configuring rate limiters, or protecting services from cascading failures. Generates circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback implementations. Validates resilience configurations through Actuator endpoints.

Provides REST API design standards and best practices for Spring Boot projects. Use when creating or reviewing REST endpoints, DTOs, error handling, pagination, security headers, HATEOAS and architecture patterns.

Provides distributed transaction patterns using the Saga Pattern for Spring Boot microservices. Use when implementing distributed transactions across services, handling compensating transactions, ensuring eventual consistency, or building choreography or orchestration-based sagas with Kafka, RabbitMQ, or Axon Framework.

Provides JWT authentication and authorization patterns for Spring Boot 3.5.x covering token generation with JJWT, Bearer/cookie authentication, database/OAuth2 integration, and RBAC/permission-based access control using Spring Security 6.x. Use when implementing authentication or authorization in Spring Boot applications.

Provides comprehensive testing patterns for Spring Boot applications covering unit, integration, slice, and container-based testing with JUnit 5, Mockito, Testcontainers, and performance optimization. Use when writing tests, @Test methods, @MockBean mocks, or implementing test suites for Spring Boot applications.

Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.

Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded databases.

Provides patterns for unit testing Spring application events. Validates event publishing with ApplicationEventPublisher, tests @EventListener annotation behavior, and verifies async event handling. Use when writing tests for event listeners, mocking application events, or verifying events were published in your Spring Boot services.

Provides patterns for unit testing Jakarta Bean Validation (JSR-380), including @Valid, @NotNull, @Min, @Max, @Email constraints with Hibernate Validator. Generates custom validator tests, constraint violation assertions, validation groups, and parameterized validation tests. Validates data integrity logic without Spring context. Use when writing validation tests, bean validation tests, or testing custom constraint validators.

Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.

Provides patterns for unit testing Spring Cache annotations (@Cacheable, @CachePut, @CacheEvict). Generates test code that mocks cache managers, verifies cache hit/miss behavior, tests cache key generation with SpEL expressions, validates eviction strategies, and checks conditional caching scenarios. Triggers: caching tests, test Spring cache, mock cache, Spring Boot caching, cache hit/miss verification, @Cacheable testing.

Provides patterns for unit testing `@ConfigurationProperties` classes with `@ConfigurationPropertiesTest`. Validates property binding, tests validation constraints, verifies default values, checks type conversions, and mocks property sources for Spring Boot configuration properties. Use when testing application configuration binding, validating YAML or application.properties files, verifying environment-specific settings, or testing nested property structures.

Provides patterns for unit testing REST controllers using MockMvc and @WebMvcTest. Generates controller tests that validates request/response mapping, validation, exception handling, and HTTP status codes. Use when testing web layer endpoints in isolation for API endpoint testing, Spring MVC tests, mock HTTP requests, or controller layer unit tests.

Provides patterns for unit testing `@ExceptionHandler` and `@ControllerAdvice` in Spring Boot applications. Validates error response formatting, mocks exceptions, verifies HTTP status codes, tests field-level validation errors, and asserts custom error payloads. Use when writing Spring exception handler tests, REST API error tests, or mocking controller advice.

Provides patterns for unit testing JSON serialization/deserialization with Jackson and `@JsonTest`. Validates JSON mapping, custom serializers, date formats, and polymorphic types. Use when testing JSON serialization, validating custom serializers, or writing JSON unit tests in Spring Boot applications.

Provides patterns for unit testing mappers, converters, and bean mappings. Validates entity-to-DTO and model transformation logic in isolation. Generates executable mapping tests with MapStruct and custom converter test coverage. Use when writing mapping tests, converter tests, entity mapping tests, or ensuring correct data transformation between DTOs and domain objects.

Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value analysis.

Provides patterns for unit testing Spring `@Scheduled` and `@Async` methods using JUnit 5, CompletableFuture, Awaitility, and Mockito. Covers mocking task execution and timing, verifying execution counts, testing cron expressions, validating retry behavior, and simulating thread pool behavior. Use when testing background tasks, cron jobs, periodic execution, scheduled tasks, or thread pool behavior.

Provides patterns for unit testing Spring Security with `@PreAuthorize`, `@Secured`, `@RolesAllowed`. Validates role-based access control and authorization policies. Use when testing security configurations and access control logic.

Provides patterns for unit testing service layer with Mockito. Creates isolated tests that mock repository calls, verify method invocations, test exception scenarios, and stub external API responses. Use when testing service behaviors and business logic without database or external services.

Provides patterns for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.

Posts review findings from a JSON file as inline comments on a GitHub Pull Request, attaching each comment to its file and line. Use when you have a list/JSON of review findings (each with a file path, line number, and a message such as summary/failure_scenario) and want them published on a PR as inline review comments. Triggers include "post these review comments on the PR", "associate comments to files in the PR", "publish review findings to PR #N", or having a JSON array of {file, line, summary} to turn into PR comments.

Скилл для очеловечивания русскоязычного текста. Убирает признаки AI-генерации, делает текст живым. Используй ВСЕГДА, когда пользователь просит: очеловечить текст, убрать следы нейросети, сделать текст живым/естественным, переписать как человек, humanize на русском, убрать канцелярит, убрать водянистость, сделать текст менее формальным. Также используй если пользователь вставляет русскоязычный текст и говорит что-то вроде 'перепиши', 'сделай лучше', 'звучит как робот', 'слишком искусственно'. Работает ТОЛЬКО с русским языком. Для английского используй оригинальный humanizer. НЕ используй для: перевод, написание с нуля, грамматика, код.

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Explore ambiguous or early-stage ideas interactively — tracks wish-readiness and crystallizes into a design for /wish.

Convene real AI agents for multi-perspective deliberation on architecture, design, and strategy decisions.

Dispatch docs subagent to audit, generate, and validate documentation against the codebase.

Batch-execute SHIP-ready wishes overnight — pick wishes, orchestrate workers, review PRs, wake up to results.

Dispatch fix subagent for FIX-FIRST gaps from /review, re-review, and escalate after 2 failed loops.

Browse, search, and contribute community hacks — real-world patterns for provider switching, teams, skills, hooks, cost optimization, and more.

Entry point for all genie operations — auto-routes natural language to the right skill, detects lifecycle state, and handles operational commands. Use when planning features, reporting bugs, managing teams, or asking about genie.

Diagnose and fix agent behavioral surfaces when the user corrects a mistake — connects to Claude native memory.

Wire a Genie agent to an Omni channel in one canonical flow — register the agent, bind to an instance, verify the round-trip. Replaces the 5+ command legacy chain.

Full PM playbook — triage backlog, prioritize, assign, track, report, escalate. Copilot, autopilot, or pair modes.

Transform a brief or prompt into a structured, production-ready prompt via prompt-optimizer. File or text mode.

Investigate bugs comprehensively — cascade through /trace, capture browser evidence, extract observability data, and auto-create a GitHub issue with all findings.

Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.

Dispatch trace subagent to investigate unknown issues — reproduces, traces, and reports root cause for /fix handoff.

Convert an idea into a structured wish plan with scope, acceptance criteria, and execution groups for /work.

Guided onboarding — scaffold workspace, shape agent identity, create first wish, execute, and celebrate.

Execute an approved wish plan — orchestrate subagents per task group with fix loops, validation, and review handoff.

Run the Antigravity CLI (Gemini) as a collaborating AI inside Claude Code, with intelligent model routing across the software development lifecycle. Claude is the conductor/orchestrator — requirements, architecture, the hard 20%, verification, and review — and routes deterministic, high-volume work (scaffolding, boilerplate, test generation, first-pass review, migrations, web/Vertex AI Search) to Antigravity (Gemini), the cheaper, faster model. Use when the user wants to "use Antigravity / agy", "vibe code / agentic engineering", "accelerate the SDLC", "delegate to Gemini", "scaffold / generate tests / migrate", "first-pass code review", "search web or internal/company data", "deep research / multi-source research report", "second-model cross-check", or "lower token cost on a big job". Claude always verifies Antigravity's output and re-checks itself if unsatisfied.

Move an existing Claude Code setup onto the Antigravity CLI (agy) — user skills, CLAUDE.md, auto-memory, MCP servers, installed plugins, permissions and trusted workspaces. Use when the user says "migrate to Antigravity", "move my Claude Code config to agy", "bring my skills/memory/MCP over", "set up agy like my Claude Code", "agy plugin import claude found nothing", or asks what can and cannot be carried across. Also use to explain the two config layouts, or to reverse a migration.

Create production-quality Android applications following Google's official architecture guidance and NowInAndroid best practices. Use when building Android apps with Kotlin, Jetpack Compose, MVVM architecture, Hilt dependency injection, Room database, or multi-module projects. Triggers on requests to create Android projects, screens, ViewModels, repositories, feature modules, or when asked about Android architecture patterns.

Link the current Claude Code session to a ticket (Linear, Jira, GitHub Issues, or GitHub Pull Requests) and cache its title/status in karma. Use when the user explicitly asks to link, attach, associate, or connect this session to a ticket, issue, or PR — e.g. "/link-ticket-to-session ABC-123", "link this session to LINEAR-42", "associate this work with issue #15", "attach session to PR octocat/repo#7". Do NOT auto-invoke from passing ticket-key mentions in normal conversation.

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Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.

Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.

Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.

Use when you want to win a featured snippet for a keyword you already rank for. The agent checks the current snippet format, analyzes your content, and rewrites the relevant section to match what Google wants.

Use when rewriting or refreshing an existing page that's underperforming. The agent fetches the URL, analyzes the current content, researches the SERP, and rewrites using the full anti-AI-slop ruleset — no data exports needed.

Use when planning to rank for a specific keyword. The agent Googles it, reads the top 10, classifies intent, reads the top 3 competitor pages, and produces a 90-day ranking plan with intent, SERP analysis, and content recommendations.

Use when planning link acquisition. Classifies the site's authority phase from site age and visible signals, then recommends phase-appropriate tactics from the bundled tactic playbook library. No backlink tool required.

Use when auditing a specific page's SEO performance, content quality, and competitive position. The agent fetches the URL, Googles the primary keyword, reads the top 3 competitors, and produces a full 7-dimension audit — no exports, no analytics access required.

Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. The agent compares the page to the top-ranking competitors and produces a specific list of entities, subtopics, and relationships to add.

Use when planning a topic cluster (hub + spokes) for a new content area. The agent researches the space, identifies the hub topic, maps the spokes, and produces a specific content plan with internal linking strategy.

Use when writing a complete SEO article. Includes the full anti-AI-slop ruleset (banned vocabulary, banned phrases, banned structural patterns) and voice rules. The agent researches the SERP itself if needed — no keyword data exports required.